A convincing paragraph is not proof that someone bought or used a product. AI can produce fluent praise, but fake or misleading reviews existed before these tools. The practical response is to evaluate evidence carefully and hold your own project to the same standard you want from others.
Read patterns, context and specifics
Look for details about the product version, task, limitations and support experience. Compare a range of recent and older reviews, including constructive criticism. Check whether the review concerns the same offer you are considering. A rating for an unrelated version or another seller may tell you little.
Consider the source and its incentives. A sponsored recommendation, affiliate article, selected testimonial and independent customer review have different contexts. Disclosure helps you interpret the relationship; it does not automatically prove the recommendation true or false.
Avoid unreliable shortcuts
Do not assume that polished writing is AI-generated or that awkward writing proves authenticity. Automated AI-detection scores are not a dependable verdict on an individual review. A verified-purchase label may establish a platform-defined transaction signal, but it does not verify every sentence or eliminate all possible incentives.
When a decision matters, consult primary product information, clear terms, a sample or demonstration and a range of independent observations. Look for accessible support and realistic limitations. Be cautious about guaranteed income claims or results presented without costs, time or relevant context.
Collect your first feedback honestly
After someone uses your project, ask what worked, what did not and what they expected. Invite a public review only where appropriate and under the platform's rules. Do not write the praise for them, buy fake reviews or ask only happy users in a way that misrepresents the overall feedback process.
If you provide a free sample or any incentive, check the applicable rules and disclose the relationship where required. U.S. FTC guidance addresses fake reviews, some sentiment-conditioned incentives and other misleading practices. Requirements differ by jurisdiction and platform, so verify the relevant rules before designing a promotion.
Use testimonials with permission
Ask before publishing a person's name, words, image or screenshot. Show the final wording if you edit for length and preserve the meaning. Keep a private record of permission and the source feedback. Do not attach a generated face to imply a real customer or turn a hypothetical example into an earnings story.
For a brand-new product without customers, show what the product does through an original demonstration. Label sample data and explain its limits. You can establish clarity and care before you have testimonials; invented social proof only creates a misleading starting point.
Make your own results understandable
| If you publish | Include |
|---|---|
| A sales figure | Period, gross or net definition, relevant costs and context |
| A time-saving example | Task, measurement method and review effort |
| A customer quote | Permission and any material relationship |
| A fictional walkthrough | A clear demonstration label |
Future review systems may add stronger identity, transaction or provenance signals. It is uncertain which will become common, and no single badge will replace judgment. Keep your evidence, correct mistakes and respond respectfully to criticism without exposing private customer details. For a small project, trust grows from doing what you said you would do and making your claims easy to understand.
Sources and useful references
Keep learning
- Plan your first traffic experiment in Campaign Studio
- Your first analytics report: what to read when the numbers are small
Your next move
Review your own claims, permissions and sample labels. Ask a real user for honest feedback without steering the sentiment.